This repository contains a small introduction to machine learning using python, scikit-learn, tensorflow and pyplot. It starts with a quick introduction to key terms used in the field and then dives in a bit into supervised learning: classification, regression. The repo comes with a dockerfile that you can use to built a docker-image that will have everything required to run.
Steps to deploy:
- git pull this repo, cd into the directory
- Create a volume 'mlwork' for the docker-container
docker volume create mlwork - Build the image ..might take a while..
docker build -f Dockerfile . -t jupyter/new-tensorflow-notebook - Start the container:
docker run --name="intro_to_ml" -d -it --rm -p 8899:8888 -v mlwork:/home/jovyan/work jupyter/new-tensorflow-notebook start-notebook.sh --NotebookApp.token='' - copy repo into the container's volume
docker cp . intro_to_ml:/home/jovyan/work - access the jupyter-notebook under http://localhost:8899
Notice: This way of starting the container disables all security-mechanisms from jupyter-notebook. This is convenient but not secure, therefore only do it in an isolated environment!